sim.create_y: Simulate noisy observations

In jtimonen/lgpr: Longitudinal Gaussian Process Regression

Simulate noisy observations

Description

Simulate noisy observations

Usage

sim.create_y(noise_type, f, snr, phi, gamma, N_trials)

Arguments

noise_type	Either "gaussian", "poisson", "nb" (negative binomial), "binomial", or "bb" (beta-binomial).
f	The underlying signal.
snr	The desired signal-to-noise ratio. This argument is valid only when noise_type is "gaussian".
phi	The inverse overdispersion parameter for negative binomial data. The variance is g + g^2/phi.
gamma	The dispersion parameter for beta-binomial data.
N_trials	The number of trials parameter for binomial data.

Value

A list out, where

• out$h is f mapped through an inverse link function (times N_trials if noise_type is binomial or beta-binomial)

• out$y is the noisy response variable.

jtimonen/lgpr documentation built on Sept. 15, 2024, 1:08 a.m.